2 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
Lightweight LIF-only SNN accelerator using differential time encoding
Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser +1
Spiking Neural Networks (SNNs) offer a promising solution to the problem of increasing computational and energy requirements for modern Machine Learning (ML) applications. Due to t…
Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding
Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser +1
Spiking Neural Networks (SNNs) have garnered attention over recent years due to their increased energy efficiency and advantages in terms of operational complexity compared to trad…
On the Solvability of the {XOR} Problem by Spiking Neural Networks
Bernhard A. Moser, Michael Lunglmayr
The linearly inseparable XOR problem and the related problem of representing binary logical gates is revisited from the point of view of temporal encoding and its solvability by sp…
On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals
Bernhard A. Moser, Michael Lunglmayr
Leaky-integrate-and-fire (LIF) is studied as a non-linear operator that maps an integrable signal to a sequence of discrete events, the spikes. In the case without any Di…